Invited review: Practical feeding management recommendations to mitigate the risk of subacute ruminal acidosis in dairy cattle
Bibliographic record
Abstract
Rumen health is of vital importance in ensuring healthy and efficient dairy cattle production. Current feeding programs for cattle recommend concentrate-rich diets to meet the high nutritional needs of cows during lactation and enhance cost-efficiency. These diets, however, can impair rumen health. The term "subacute ruminal acidosis" (SARA) is often used as a synonym for poor rumen health. In this review, we first describe the physiological demands of cattle for dietary physically effective fiber. We also provide background information on the importance of enhancing salivary secretions and short-chain fatty acid absorption across the stratified squamous epithelium of the rumen; thus, preventing the disruption of the ruminal acid-base balance, a process that paves the way for acidification of the rumen. On-farm evaluation of dietary fiber adequacy is challenging for both nutritionists and veterinarians; therefore, this review provides practical recommendations on how to evaluate the physical effectiveness of the diet based on differences in particle size distribution, fiber content, and the type of concentrate fed, both when the latter is part of total mixed ration and when it is supplemented in partial mixed rations. Besides considering the absolute amount of physically effective fiber and starch types in the diet, we highlight the role of several feeding management factors that affect rumen health and should be considered to control and mitigate SARA. Most importantly, transitional feeding to ensure gradual adaptation of the ruminal epithelium and microbiota; monitoring and careful management of particle size distribution; controlling feed sorting, meal size, and meal frequency; and paying special attention to primiparous cows are some of the feeding management tools that can help in sustaining rumen health in high-producing dairy herds. Supplementation of feed additives including yeast products, phytogenic compounds, and buffers may help attenuate SARA, especially during stress periods when the risk of a deficiency of physically effective fiber in the diet is high, such as during early lactation. However, the usage of feed additives cannot fully compensate for suboptimal feeding management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".